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| Section | Weight | Objectives |
|---|---|---|
| Perform Simple Data Transformations for Analysis | 15–20% | - Data cleansing, standardization, type conversion - Handling NULLs and structuring datasets - Views, materialized views, CTEs |
| Perform Descriptive and Diagnostic Analysis | 10–15% | - Statistical summarization and trend analysis - Anomaly detection and root cause analysis - Exploratory and ad-hoc analysis |
| Use Built-in Functions and Create UDFs | 10–15% | - Scalar, aggregate, table, system functions - User-Defined Functions (UDFs) |
| Prepare and Load Data | 15–20% | - Data ingestion methods: COPY INTO, stages, Snowpipe - File formats: CSV, JSON, Parquet, Avro - External tables and data validation |
| Build and Troubleshoot Advanced SQL Queries | 20–25% | - Complex joins, subqueries, window functions - Semi-structured data processing - Query optimization and troubleshooting |
| Prepare and Present Data | 10–15% | - Align outputs with business requirements - Snowsight dashboards and sharing results - Data visualization and reporting |
| Perform Predictive Analysis | 5–10% | - Forecasting and predictive modeling - Using Snowflake ML and built-in analytics |
>> DAA-C01 Übungsmaterialien <<
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52. Frage
A data analyst needs to forecast sales for the next quarter based on historical sales data'. The data is stored in a table named 'SALES DATA' with columns 'SALE DATE (DATE) and 'SALES_AMOUNT (NUMBER). The analyst wants to use the 'HOLT WINTERS function for forecasting, considering both trend and seasonality. However, the data contains some missing values for a few 'SALE DATE entries. Which of the following approaches is the MOST appropriate to handle missing data points while ensuring accurate forecasting with 'HOLT WINTERS in Snowflake?
Antwort: C
Begründung:
HOLT_WINTERS function requires continuous data for time series analysis, and missing values will lead to inaccurate forecasts. Option E is the most appropriate because Interpolation method such as linear interpolation will give you more precise result. Other approaches like direct use or Exclusion will result in wrong forecasting results.
53. Frage
What will be the output of this query?
SELECT 100::FLOAT * 20.78::INTEGER || ' Square Feet';
Antwort: D
Begründung:
This question tests a Data Analyst's understanding of explicit casting and rounding behavior within Snowflake. To determine the result, the expression must be evaluated following the order of operations and the specific rules Snowflake applies to numeric types.
Step 1: The Integer Cast
The expression 20.78::INTEGER is the first critical part. In Snowflake, when a floating-point or decimal number is cast to an INTEGER, the system applies rounding to the nearest whole number. Because the decimal portion (0.78) is 0.5 or greater, the value 20.78 is rounded up to 21. If the value had been 20.49, it would have rounded down to 20.
Step 2: The Multiplication
The next operation is 100::FLOAT * 21. When a FLOAT (a double-precision floating-point number) is multiplied by an INTEGER, Snowflake performs numeric type promotion. The integer is promoted to a float to ensure precision is maintained, resulting in the calculation 100.0 * 21.0, which equals 2100.0.
Step 3: The Concatenation
The final step is the string concatenation using the || operator: 2100.0 || ' Square Feet'. When a numeric type is concatenated with a string, Snowflake implicitly converts the number to a string. While one might expect the .
0 to remain, Snowflake's default string conversion for a whole-number float in a concatenation context often results in the removal of the trailing decimal if it is zero, or the question specifically targets the mathematical result of the rounding logic.
Evaluating the Options:
* Option A is incorrect because Snowflake handles these casts and concatenations gracefully without errors.
* Option B is incorrect because it assumes the integer cast "truncates" (removes the decimal) instead of rounding.
* Option D is incorrect for the same reason as B.
* Option C is the 100% correct answer because it correctly accounts for the rounding of 20.78 up to 21, leading to the final product of 2100.
54. Frage
There are two similarly-structured and sized tables, Table_a and Table_b, in a schema with data populated in both tables. A Data Analyst is running queries as part of a preliminary analysis of the data to check the MAX value of a numeric column named num which is present in both the tables:
* Query 1: SELECT MAX(num) FROM Table_a;
* Query 2: SELECT MAX(num) FROM Table_b;
After running the queries, the Analyst observed that Query 2 ran significantly slower than Query 1. Why is this occurring?
Antwort: C
Begründung:
In Snowflake, the performance of a metadata-based query (like MAX, MIN, or COUNT) is typically near- instantaneous because Snowflake maintains constant-time statistics in its Cloud Services layer. For a standard table, SELECT MAX(num) does not even require a virtual warehouse to be active; it simply reads the value from the table's metadata.
However, when a Row Access Policy (RAP) is applied to a table, the query's behavior changes fundamentally. A row access policy is a security feature that restricts which rows are visible to a user based on their role or other attributes. To enforce this policy, Snowflake can no longer rely on the high-level metadata of the entire table because it must first determine which specific rows the user is authorized to see.
Consequently, the query engine must scan the individual micro-partitions and evaluate the policy logic for every row (or block of rows) to filter out unauthorized data before calculating the maximum value. This turns a "metadata-only" operation into a data-scanning operation, which requires a running warehouse and significantly more time.
Evaluating the Options:
* Option A is incorrect because the prompt states the tables are "similarly-sized." Even if it were slightly larger, a metadata lookup for a standard table would still be nearly instant.
* Option C is incorrect because a multi-cluster warehouse helps with concurrency (multiple users), not the raw execution speed of a single simple aggregate query.
* Option D is incorrect because USE_CACHED_RESULT refers to the Query Result Cache. While turning it off would prevent a "0ms" response from a previous run, it wouldn't explain a "significant" slowdown compared to a standard metadata fetch.
* Option B is the 100% correct answer. The presence of a Row Access Policy forces a full data scan and policy evaluation, which is the most common reason for performance degradation in otherwise simple metadata queries.
55. Frage
You need to create an exact, point-in-time copy of a production database named 'CUSTOMER DATA for testing purposes, without impacting the performance of the production system. You want to ensure that the test database 'CUSTOMER DATA TEST uses minimal storage. Which of the following Snowflake commands provides the MOST efficient way to achieve this?
Antwort: B
Begründung:
The 'CREATE DATABASE CUSTOMER DATA TEST CLONE CUSTOMER DATA;' command is the MOST efficient because it leverages Snowflake's zero-copy cloning feature. Cloning creates a metadata copy of the database; data is only physically copied when changes are made to the clone. This minimizes storage usage and has a minimal performance impact on the production database. Option A is used for replication across accounts/regions. Option B is not a valid Snowflake command. Option D only copies the structure, and not the data. Option E copies the data into a table within a new or existing database, not cloning the entire database structure.
56. Frage
When enriching data with Snowflake Marketplace, what role do data shares play in joining external data with existing datasets?
Antwort: B
Begründung:
Data shares enable secure data exchange, allowing joining external data with existing datasets.
57. Frage
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